1. Field of the Invention
The present invention relates to a signal processing device, signal processing program, and signal processing method for reducing noise in an image signal.
2. Description of the Related Art
Conventionally known are the following two types of methods for removing noise in image data.
A first method employs a median filter to smooth singular points. More specifically, the method provides the median value of densities of local areas as an output density. For example, with 3 by 3 areas, nine values are sorted in ascending order and the fifth smallest value that is the median value is determined. Filtering image data with such a median filter allows noises such as bright or dark spots to be removed from the image data as singular points.
The second method subtracts a fixed pattern noise image from a field image pixel by pixel. The field image is image data obtained by capturing the image of a subject with an image sensor. On the other hand, the fixed pattern noise image is image data which includes only noises output from an image sensor under a light-receiving environment such as in a dark condition. This subtracting processing allows the fixed pattern noise to be removed from the subject image in phase, thereby providing an image data with its fixed pattern noise removed.
Now, the second method is described below using a mathematical expression.S(m)=So(m)+N(m),
where S(m) is the signal level of each pixel in a subject image, So(m) is the actual signal level of each pixel, N(m) is the noise level of each pixel, and m is the serial number of pixels.
The detail of the noise level is given byN(m)=Nr(m)+Nf(m),
where Nr(m) is random noise and Nf(m) is the fixed pattern noise.
In general, the longer the exposure time t of the image sensor, the stronger the fixed pattern noise. For this reason, an elongated exposure results in Nf>>Nr, causing the fixed pattern noise to be dominant. In such a case, subtracting the fixed pattern image from the field image givesSo(m)≈S(m)−Nf(m).That is, it is possible to obtain image data close to a true pixel value.
However, the aforementioned first and second methods for removing the fixed pattern noise have the following problems while it can remove fixed pattern noises to some extent.
In the first method, it is necessary to determine respective median values of each of an enormous number of pixels while referring to pixel values in local areas. This results in processing times elongated a great deal or otherwise requires dedicated hardware to be prepared.
Additionally, in the first method, the values of all pixels are to be replaced with their respective median values, thereby easily causing interpolation mistakes to be made and leading to pseudo color or pattern.
On the other hand, the second method allows subtracting processing to be carried out pixel by pixel, thereby making it possible to easily perform data processing without preparing special hardware. However, there is a problem that a fixed pattern noise approximately equal in level to an image signal cannot sufficiently remove the fixed pattern noise.